Integrating Fuzzy Rule Based Reasoning with Machine Learning for Intelligent and Scalable Irrigation Control

This study presents a hybrid intelligent irrigation decision support system that integrates fuzzy logic and Random Forest (RF) regression to support irrigation decision-making in agriculture. A fuzzy inference engine was designed using expert-defined rules based on soil moisture, air temperature, and rainfall. This system generated irrigation recommendations on a scale of 0 to 10 using Mamdani-type fuzzy logic. An RF model was trained on the same inputs with the fuzzy output as the target to evaluate how accurately a data-driven model can reproduce the fuzzy decision mapping. Using five-fold cross-validation, the RF achieved a root mean squared error (RMSE) of 0.2459 , mean absolute error (MAE) of 0.0801 , and coefficient of determination (R2) of 0.9682, indicating strong alignment with fuzzy decision logic. This hybrid approach offers a transparent, data-driven, and scalable solution, effectively bridging the gap between interpretability and prediction accuracy. The system has the potential to support precision irrigation and informed water management decisions and was evaluated using one year of daily aggregated Internet of Things (IoT) sensor observations from Narrabri, Australia.